184 lines
6.0 KiB
Python
184 lines
6.0 KiB
Python
# Code adapted from: https://github.com/Cadene/pretrained-models.pytorch
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import math
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from collections import OrderedDict
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from itertools import chain
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import torch.nn as nn
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from torch.utils import model_zoo
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from utils import Flatten
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class SEModule(nn.Module):
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def __init__(self, channels, reduction):
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super(SEModule, self).__init__()
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self.avg_pool = nn.AdaptiveAvgPool2d(1)
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self.fc1 = nn.Conv2d(channels, channels // reduction, kernel_size=1, padding=0)
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self.relu = nn.ReLU(inplace=True)
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self.fc2 = nn.Conv2d(channels // reduction, channels, kernel_size=1, padding=0)
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self.sigmoid = nn.Sigmoid()
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def forward(self, x):
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module_input = x
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x = self.avg_pool(x)
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x = self.fc1(x)
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x = self.relu(x)
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x = self.fc2(x)
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x = self.sigmoid(x)
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return module_input * x
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class SEResNeXtBottleneck(nn.Module):
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"""
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ResNeXt bottleneck type C with a Squeeze-and-Excitation module.
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"""
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expansion = 4
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def __init__(self, inplanes, planes, groups, reduction, stride=1, downsample=None, base_width=4):
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super(SEResNeXtBottleneck, self).__init__()
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width = math.floor(planes * (base_width / 64)) * groups
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self.conv1 = nn.Conv2d(inplanes, width, kernel_size=1, bias=False, stride=1)
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self.bn1 = nn.BatchNorm2d(width)
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self.conv2 = nn.Conv2d(width, width, kernel_size=3, stride=stride, padding=1, groups=groups, bias=False)
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self.bn2 = nn.BatchNorm2d(width)
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self.conv3 = nn.Conv2d(width, planes * 4, kernel_size=1, bias=False)
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self.bn3 = nn.BatchNorm2d(planes * 4)
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self.relu = nn.ReLU(inplace=True)
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self.se_module = SEModule(planes * 4, reduction=reduction)
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self.downsample = downsample
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self.stride = stride
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def forward(self, x):
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residual = x
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out = self.conv1(x)
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out = self.bn1(out)
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out = self.relu(out)
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out = self.conv2(out)
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out = self.bn2(out)
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out = self.relu(out)
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out = self.conv3(out)
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out = self.bn3(out)
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if self.downsample is not None:
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residual = self.downsample(x)
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out = self.se_module(out) + residual
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out = self.relu(out)
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return out
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class SENet(nn.Module):
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def __init__(self, block, layers, groups, reduction, inplanes=128,
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downsample_kernel_size=3, downsample_padding=1):
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super(SENet, self).__init__()
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self.inplanes = inplanes
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layer0_modules = [
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('conv1', nn.Conv2d(3, inplanes, kernel_size=7, stride=2, padding=3, bias=False)),
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('bn1', nn.BatchNorm2d(inplanes)),
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('relu1', nn.ReLU(inplace=True)),
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# To preserve compatibility with Caffe weights `ceil_mode=True`
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# is used instead of `padding=1`.
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('pool', nn.MaxPool2d(3, stride=2, ceil_mode=True))
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]
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self.layer0 = nn.Sequential(OrderedDict(layer0_modules))
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self.layer1 = self._make_layer(
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block,
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planes=64,
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blocks=layers[0],
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groups=groups,
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reduction=reduction,
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downsample_kernel_size=1,
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downsample_padding=0
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)
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self.layer2 = self._make_layer(
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block,
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planes=128,
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blocks=layers[1],
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stride=2,
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groups=groups,
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reduction=reduction,
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downsample_kernel_size=downsample_kernel_size,
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downsample_padding=downsample_padding
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)
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self.layer3 = self._make_layer(
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block,
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planes=256,
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blocks=layers[2],
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stride=2,
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groups=groups,
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reduction=reduction,
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downsample_kernel_size=downsample_kernel_size,
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downsample_padding=downsample_padding
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)
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self.layer4 = self._make_layer(
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block,
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planes=512,
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blocks=layers[3],
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stride=2,
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groups=groups,
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reduction=reduction,
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downsample_kernel_size=downsample_kernel_size,
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downsample_padding=downsample_padding
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)
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self.cls = nn.Sequential(
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nn.AdaptiveAvgPool2d(1),
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Flatten(),
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nn.Linear(512 * block.expansion, 1)
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)
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def _make_layer(self, block, planes, blocks, groups, reduction, stride=1,
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downsample_kernel_size=1, downsample_padding=0):
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downsample = None
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if stride != 1 or self.inplanes != planes * block.expansion:
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downsample = nn.Sequential(
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nn.Conv2d(self.inplanes, planes * block.expansion,
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kernel_size=downsample_kernel_size, stride=stride,
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padding=downsample_padding, bias=False),
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nn.BatchNorm2d(planes * block.expansion),
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)
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layers = [block(self.inplanes, planes, groups, reduction, stride, downsample)]
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self.inplanes = planes * block.expansion
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for i in range(1, blocks):
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layers.append(block(self.inplanes, planes, groups, reduction))
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return nn.Sequential(*layers)
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def paramgroup01(self):
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return chain(
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self.layer0.parameters(),
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self.layer1.parameters(),
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)
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def paramgroup234(self):
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return chain(
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self.layer2.parameters(),
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self.layer3.parameters(),
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self.layer4.parameters(),
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)
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def parameters_classifier(self):
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return self.cls.parameters()
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def forward(self, x):
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x = self.layer0(x)
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x = self.layer1(x)
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x = self.layer2(x)
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x = self.layer3(x)
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x = self.layer4(x)
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c = self.cls(x)
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return c
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def get_model():
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model = SENet(SEResNeXtBottleneck, [3, 4, 6, 3], groups=32, reduction=16, inplanes=64,
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downsample_kernel_size=1, downsample_padding=0)
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checkpoint = model_zoo.load_url('http://data.lip6.fr/cadene/pretrainedmodels/se_resnext50_32x4d-a260b3a4.pth')
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model.load_state_dict(checkpoint, strict=False)
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return model
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